M
Mykel J. Kochenderfer
Researcher at Stanford University
Publications - 449
Citations - 12534
Mykel J. Kochenderfer is an academic researcher from Stanford University. The author has contributed to research in topics: Computer science & Markov decision process. The author has an hindex of 41, co-authored 388 publications receiving 8215 citations. Previous affiliations of Mykel J. Kochenderfer include Massachusetts Institute of Technology & University of Edinburgh.
Papers
More filters
Book ChapterDOI
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
TL;DR: In this paper, the authors presented a scalable and efficient technique for verifying properties of deep neural networks (or providing counter-examples) based on the simplex method, extended to handle the non-convex Rectified Linear Unit (ReLU) activation function.
Book ChapterDOI
Cooperative Multi-agent Control Using Deep Reinforcement Learning
TL;DR: It is shown that policy gradient methods tend to outperform both temporal-difference and actor-critic methods and that curriculum learning is vital to scaling reinforcement learning algorithms in complex multi-agent domains.
Posted Content
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
TL;DR: Results show that the novel, scalable, and efficient technique presented can successfully prove properties of networks that are an order of magnitude larger than the largest networks verified using existing methods.
Book
Decision Making Under Uncertainty: Theory and Application
Mykel J. Kochenderfer,Christopher Amato,Girish Chowdhary,Jonathan P. How,Hayley J. Davison Reynolds,Jason R. Thornton,Pedro A. Torres-Carrasquillo,N. Kemal Ure,John Vian +8 more
TL;DR: This book provides an introduction to the challenges of decision making under uncertainty from a computational perspective and presents both the theory behind decision making models and algorithms and a collection of example applications that range from speech recognition to aircraft collision avoidance.
Book ChapterDOI
The Marabou Framework for Verification and Analysis of Deep Neural Networks
Guy Katz,Derek A. Huang,Duligur Ibeling,Kyle D. Julian,Christopher Lazarus,Rachel Lim,Parth Shah,Shantanu Thakoor,Haoze Wu,Aleksandar Zeljić,David L. Dill,Mykel J. Kochenderfer,Clark Barrett +12 more
TL;DR: Marabou is an SMT-based tool that can answer queries about a network’s properties by transforming these queries into constraint satisfaction problems, and it performs high-level reasoning on the network that can curtail the search space and improve performance.